'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
  from PIL import Image




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Dict				="naver-clova-ix/donut-base-finetuned-docvqa"
     lowerCamelCase    :	Optional[Any]				=(
         "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
         "should be the document containing the information, as well as a `question` that is the question about the "
         "document. It returns a text that contains the answer to the question."
     )
     lowerCamelCase    :	List[str]				="document_qa"
     lowerCamelCase    :	str				=AutoProcessor
     lowerCamelCase    :	Tuple				=VisionEncoderDecoderModel

     lowerCamelCase    :	Optional[int]				=["image", "text"]
     lowerCamelCase    :	Tuple				=["text"]






     def __init__(					self						:						int    ,							*a						:						int    ,							**a						:						Optional[Any]       ):





         """simple docstring"""



         if not is_vision_available():
             raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''       )

         super().__init__(*a    ,							**a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						"Image"    ,							a						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
         __lowerCamelCase            =    task_prompt.replace('''{user_input}'''    ,							a       )
         __lowerCamelCase            =    self.pre_processor.tokenizer(
             a    ,							add_special_tokens=a    ,							return_tensors='''pt'''       ).input_ids
         __lowerCamelCase            =    self.pre_processor(a    ,							return_tensors='''pt'''       ).pixel_values

         return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						Optional[int]       ):





         """simple docstring"""



         return self.model.generate(
             inputs['''pixel_values'''].to(self.device       )    ,							decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device       )    ,							max_length=self.model.decoder.config.max_position_embeddings    ,							early_stopping=a    ,							pad_token_id=self.pre_processor.tokenizer.pad_token_id    ,							eos_token_id=self.pre_processor.tokenizer.eos_token_id    ,							use_cache=a    ,							num_beams=1    ,							bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]    ,							return_dict_in_generate=a    ,							).sequences






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    self.pre_processor.batch_decode(a       )[0]
         __lowerCamelCase            =    sequence.replace(self.pre_processor.tokenizer.eos_token    ,							''''''       )
         __lowerCamelCase            =    sequence.replace(self.pre_processor.tokenizer.pad_token    ,							''''''       )
         __lowerCamelCase            =    re.sub(R'''<.*?>'''    ,							''''''    ,							a    ,							count=1       ).strip()  # remove first task start token
         __lowerCamelCase            =    self.pre_processor.tokenajson(a       )

         return sequence["answer"]

'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
